Face Recognition Based Attendance System

Abstract

In colleges, universities, organizations, schools, and offices, taking attendance is an important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one making our task easy. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, section, and photographs, is trained and is also available for teachers and staff attendance. The images are extracted using Open CV a library of python. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.